OpenRaiser/Intern-Atlas huggingface dataset:
Mobile Phone Metadata for Development
Venue: arXiv cs.CY 2018
Year: 2018

Mobile phones are now widely adopted by most of the world population. Each time a call is made (or an SMS sent), a Call Detail Record (CDR) is generated by the telecom companies for billing purpose. These metadata provide information on when, how, from where and with whom we communicate. Conceptually, they can be described as a geospatial, dynamic, weighted and directed network. Applications of CDRs for development are numerous. They have been used to model the spread of infectious diseases, study road traffic, support electrification planning strategies or map socio-economic level of population. While massive, CDRs are not statistically representative of the whole population due to several sources of bias (market, usage, spatial and temporal resolution). Furthermore, mobile phone metadata are held by telecom companies. Consequently, their access is not necessarily straightforward and can seriously hamper any operational application. Finally, a trade-off exists between privacy and utility when using sensitive data like CDRs. New initiatives such as Open Algorithm might help to deal with these fundamental questions by allowing researchers to run algorithms on the data that remain safely stored behind the firewall of the providers.
A SDN-based Flexible System for On-the-Fly Monitoring and Treatment of Security Events
Venue: arXiv cs.CR 2018
Year: 2018

The Software Defined Networking (SDN) paradigm decouples control and data planes, offering high programmability and a global view of the network. However, it is a challenge not only provide security in these next generation networks as well as allow that network attacks could be subjected to an incident and forensic treatment procedure. This paper proposes the implementation of flexible mechanisms of monitoring and treatment of security events categorized per type of attack and associated with whitelist and blacklist resources by means of the SDN controller programmability. The resources to perform intrusion and attack analysis are validated by means of a real SDN/OpenFlow testbed.
Mixed integer nonlinear programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations
Venue: arXiv cs.SY 2018
Year: 2018

The problem of joint coordination of plug-in electric vehicles (PEVs) charging and grid power control is to minimize both PEVs charging cost and energy generation cost while meeting both residential and PEVs' power demands and suppressing the potential impact of PEVs integration. A bang-bang PEV charging strategy is adopted to exploit its simple online implementation, which requires computation of a mixed integer nonlinear programming problem (MINP) in binary variables of the PEV charging strategy and continuous variables of the grid voltages. A new solver for this MINP is proposed. Its efficiency is shown by numerical simulations.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
Venue: arXiv cs.AI 2018
Year: 2018

This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.
ChangeMyView Through Concessions: Do Concessions Increase Persuasion?
Venue: arXiv cs.CL 2018
Year: 2018

In discourse studies concessions are considered among those argumentative strategies that increase persuasion. We aim to empirically test this hypothesis by calculating the distribution of argumentative concessions in persuasive vs. non-persuasive comments from the ChangeMyView subreddit. This constitutes a challenging task since concessions are not always part of an argument. Drawing from a theoretically-informed typology of concessions, we conduct an annotation task to label a set of polysemous lexical markers as introducing an argumentative concession or not and we observe their distribution in threads that achieved and did not achieve persuasion. For the annotation, we used both expert and novice annotators. With the ultimate goal of conducting the study on large datasets, we present a self-training method to automatically identify argumentative concessions using linguistically motivated features. We achieve a moderate F1 of 57.4% on the development set and 46.0% on the test set via the self-training method. These results are comparable to state of the art results on similar tasks of identifying explicit discourse connective types from the Penn Discourse Treebank. Our findings from the manual labeling and the classification experiments indicate that the type of argumentative concessions we investigated is almost equally likely to be used in winning and losing arguments from the ChangeMyView dataset. While this result seems to contradict theoretical assumptions, we provide some reasons for this discrepancy related to the ChangeMyView subreddit.
Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies
Venue: arXiv cs.CY 2018
Year: 2018

A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluation of quality of similarity measures is difficult. To this end, we propose an evaluation approach utilizing three levels of abstraction. We illustrate our approach to measuring similarity and provide evaluation using items from three diverse programming environments.
An Optimal Algorithm for Online Unconstrained Submodular Maximization
Venue: arXiv cs.LG 2018
Year: 2018

We consider a basic problem at the interface of two fundamental fields: submodular optimization and online learning. In the online unconstrained submodular maximization (online USM) problem, there is a universe $[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily monotone) submodular functions arrive over time. The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret algorithm for this problem, meaning that for every sequence of nonnegative submodular functions, the algorithm's expected total value is at least $1/2$ times that of the best subset in hindsight, up to an error term sublinear in $T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online algorithm when the submodular functions are presented as value oracles. Previous work on the offline problem implies that picking a subset uniformly at random in each time step achieves zero $1/4$-regret. A byproduct of our techniques is an explicit subroutine for the two-experts problem that has an unusually strong regret guarantee: the total value of its choices is comparable to twice the total value of either expert on rounds it did not pick that expert. This subroutine may be of independent interest.
Self-supervisory Signals for Object Discovery and Detection
Venue: arXiv cs.CV 2018
Year: 2018

In robotic applications, we often face the challenge of discovering new objects while having very little or no labelled training data. In this paper we explore the use of self-supervision provided by a robot traversing an environment to learn representations of encountered objects. Knowledge of ego-motion and depth perception enables the agent to effectively associate multiple object proposals, which serve as training data for learning object representations from unlabelled images. We demonstrate the utility of this representation in two ways. First, we can automatically discover objects by performing clustering in the learned embedding space. Each resulting cluster contains examples of one instance seen from various viewpoints and scales. Second, given a small number of labeled images, we can efficiently learn detectors for these labels. In the few-shot regime, these detectors have a substantially higher mAP of 0.22 compared to 0.12 of off-the-shelf standard detectors trained on this limited data. Thus, the proposed self-supervision results in effective environment specific object discovery and detection at no or very small human labeling cost.
Joint Actuator-sensor Design for Stochastic Linear Systems
Venue: arXiv cs.SY 2018
Year: 2018

We investigate the joint actuator-sensor design problem for stochastic linear control systems. Specifically, we address the problem of identifying a pair of sensor and actuator which gives rise to the minimum expected value of a quadratic cost. It is well known that for the linear-quadratic-Gaussian (LQG) control problem, the optimal feedback control law can be obtained via the celebrated separation principle. Moreover, if the system is stabilizable and detectable, then the infinite-horizon time-averaged cost exists. But such a cost depends on the placements of the sensor and the actuator. We formulate in the paper the optimization problem about minimizing the time-averaged cost over admissible pairs of actuator and sensor under the constraint that their Euclidean norms are fixed. The problem is non-convex and is in general difficult to solve. We obtain in the paper a gradient descent algorithm (over the set of admissible pairs) which minimizes the time-averaged cost. Moreover, we show that the algorithm can lead to a unique local (and hence global) minimum point under certain special conditions.
Deterministic Stretchy Regression
Venue: arXiv cs.LG 2018
Year: 2018

An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper. The solution of this ridge regression with stretchable parameters is given in primal and dual spaces and in closed-form. Essentially, the proposed solution stretches the covariance computation by a power term, thereby compressing or amplifying the estimation parameters. To maintain the computation of power root terms within the real space, an input transformation is proposed. The results of an empirical evaluation in both synthetic and real-world data illustrate that the proposed method is effective for compressive learning with high-dimensional data.
Explainable Deterministic MDPs
Venue: arXiv cs.LG 2018
Year: 2018

We present a method for a certain class of Markov Decision Processes (MDPs) that can relate the optimal policy back to one or more reward sources in the environment. For a given initial state, without fully computing the value function, q-value function, or the optimal policy the algorithm can determine which rewards will and will not be collected, whether a given reward will be collected only once or continuously, and which local maximum within the value function the initial state will ultimately lead to. We demonstrate that the method can be used to map the state space to identify regions that are dominated by one reward source and can fully analyze the state space to explain all actions. We provide a mathematical framework to show how all of this is possible without first computing the optimal policy or value function.
An Overview Of Some Semantic And Syntactic Complexity Classes
Venue: arXiv cs.CC 2018
Year: 2018

We review some semantic and syntactic complexity classes that were introduced to better understand the relationship between complexity classes P and NP. We also define several new complexity classes, some of which are associated with Mersenne numbers, and show their location in the complexity hierarchy.
A Taxonomy of Malicious Traffic for Intrusion Detection Systems
Venue: arXiv cs.CR 2018
Year: 2018

With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets.
What Knowledge is Needed to Solve the RTE5 Textual Entailment Challenge?
Venue: arXiv cs.CL 2018
Year: 2018

This document gives a knowledge-oriented analysis of about 20 interesting Recognizing Textual Entailment (RTE) examples, drawn from the 2005 RTE5 competition test set. The analysis ignores shallow statistical matching techniques between T and H, and rather asks: What would it take to reasonably infer that T implies H? What world knowledge would be needed for this task? Although such knowledge-intensive techniques have not had much success in RTE evaluations, ultimately an intelligent system should be expected to know and deploy this kind of world knowledge required to perform this kind of reasoning. The selected examples are typically ones which our RTE system (called BLUE) got wrong and ones which require world knowledge to answer. In particular, the analysis covers cases where there was near-perfect lexical overlap between T and H, yet the entailment was NO, i.e., examples that most likely all current RTE systems will have got wrong. A nice example is #341 (page 26), that requires inferring from "a river floods" that "a river overflows its banks". Seems it should be easy, right? Enjoy!
Frontier Based Exploration for Autonomous Robot
Venue: arXiv cs.RO 2018
Year: 2018

Exploration is process of selecting target points that yield the biggest contribution to a specific gain function at an initially unknown environment. Frontier-based exploration is the most common approach to exploration, wherein frontiers are regions on the boundary between open space and unexplored space. By moving to a new frontier, we can keep building the map of the environment, until there are no new frontiers left to detect. In this paper, an autonomous frontier-based exploration strategy, namely Wavefront Frontier Detector (WFD) is described and implemented on Gazebo Simulation Environment as well as on hardware platform, i.e. Kobuki TurtleBot using Robot Operating System (ROS). The advantage of this algorithm is that the robot can explore large open spaces as well as small cluttered spaces. Further, the map generated from this technique is compared and validated with the map generated using turtlebot_teleop ROS Package.
A Simplified Active Calibration algorithm for Focal Length Estimation
Venue: arXiv cs.CV 2018
Year: 2018

We introduce new linear mathematical formulations to calculate the focal length of a camera in an active platform. Through mathematical derivations, we show that the focal lengths in each direction can be estimated using only one point correspondence that relates images taken before and after a degenerate rotation of the camera. The new formulations will be beneficial in robotic and dynamic surveillance environments when the camera needs to be calibrated while it freely moves and zooms. By establishing a correspondence between only two images taken after slightly panning and tilting the camera and a reference image, our proposed Simplified Calibration Method is able to calculate the focal length of the camera. We extensively evaluate the derived formulations on a simulated camera, 3D scenes and real-world images. Our error analysis over simulated and real images indicates that the proposed Simplified Active Calibration formulation estimates the parameters of a camera with low error rates.
A note on the security of CSIDH
Venue: arXiv cs.CR 2018
Year: 2018

We propose an algorithm for computing an isogeny between two elliptic curves $E_1,E_2$ defined over a finite field such that there is an imaginary quadratic order $\mathcal{O}$ satisfying $\mathcal{O}\simeq \operatorname{End}(E_i)$ for $i = 1,2$. This concerns ordinary curves and supersingular curves defined over $\mathbb{F}_p$ (the latter used in the recent CSIDH proposal). Our algorithm has heuristic asymptotic run time $e^{O\left(\sqrt{\log(|Δ|)}\right)}$ and requires polynomial quantum memory and $e^{O\left(\sqrt{\log(|Δ|)}\right)}$ classical memory, where $Δ$ is the discriminant of $\mathcal{O}$. This asymptotic complexity outperforms all other available method for computing isogenies. We also show that a variant of our method has asymptotic run time $e^{\tilde{O}\left(\sqrt{\log(|Δ|)}\right)}$ while requesting only polynomial memory (both quantum and classical).
Complex network representation through multi-dimensional node projection
Venue: arXiv cs.SI 2018
Year: 2018

Complex network topology might get pretty complicated challenging many network analysis objectives, such as community detection for example. This however makes common emergent network phenomena such as scale-free topology or small-world property even more intriguing. In the present proof-of-concept paper we propose a simple model of network representation inspired by a signal transmission physical analogy, which is apparently capable of reproducing both of the above phenomena. The model appears to be general enough to represent and/or approximate arbitrary complex networks. We propose an approach constructing such a representation by projecting each node into a multi-dimensional space of signal spectrum vectors, where network topology is induced by their overlaps. As one of the implications this enables reducing community detection in complex networks to a straightforward clustering over the projection space, for which multiple efficient approaches are available. We believe such a network representation could turn out to be a useful tool for multiple network analysis objectives.
Collaboration Diversity and Scientific Impact
Venue: arXiv cs.DL 2018
Year: 2018

The shift from individual effort to collaborative output has benefited science, with scientific work pursued collaboratively having increasingly led to more highly impactful research than that pursued individually. However, understanding of how the diversity of a collaborative team influences the production of knowledge and innovation is sorely lacking. Here, we study this question by breaking down the process of scientific collaboration of 32.9 million papers over the last five decades. We find that the probability of producing a top-cited publication increases as a function of the diversity of a team of collaborators---namely, the distinct number of institutions represented by the team. We discover striking phenomena where a smaller, yet more diverse team is more likely to generate highly innovative work than a relatively larger team within one institution. We demonstrate that the synergy of collaboration diversity is universal across different generations, research fields, and tiers of institutions and individual authors. Our findings suggest that collaboration diversity strongly and positively correlates with the production of scientific innovation, giving rise to the potential revolution of the policies used by funding agencies and authorities to fund research projects, and broadly the principles used to organize teams, organizations, and societies.
Towards Completely Characterizing the Complexity of Boolean Nets Synthesis
Venue: arXiv cs.CC 2018
Year: 2018

Boolean nets are Petri nets that permit at most one token per place. Research has approached this important subject in many ways which resulted in various different classes of boolean nets. But yet, they are only distinguished by the allowed interactions between places and transitions, that is, the possible effects of firing transitions. There are eight different interactions: no operation (nop), input (inp), output (out), set, reset (res), swap, test of occupation (used), and test of disposability (free). Considering every combination for a possible net class yields 256 boolean classes in total. The synthesis problem for a particular class is to take an automaton A and compute a boolean net of that class that has a state graph isomorphic to A. To the best of our knowledge, the computational complexity of this problem has been analyzed for just two of the 256 classes, namely elementary nets systems (nop, inp, out), where the problem is NP-hard, and flip-flop nets (nop, inp, out, swap), which are synthesizable in polynomial time. However, depending on the desired net features, like read-only places, exception handling, or hierarchy, one has to synthesize nets with other interactions as for instance contextual nets (nop, inp, out, used, free) or trace nets (nop, inp, out, set, res, used, free). The contribution of this paper is a thorough investigation of the synthesis complexity for the 128 boolean net classes that allow nop. Our main result is a general proof scheme that identifies 77 NP-hard cases. All remaining 51 classes are shown to be synthesizable in polynomial time where 35 of them turn out to be trivial.
Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter
Venue: arXiv cs.CV 2018
Year: 2018

Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state. However, its performance suffers due to inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon Crow Search Optimization to overcome low performing particles detected as outlier. Proposed outlier detection mechanism with transductive reliability achieve faster convergence of proposed PF tracking framework. In addition, we present an adaptive fuzzy fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using proposed fusion model not only enhances performance of resampling method but also achieve optimal state estimation. Performance of the proposed tracker is evaluated over 12 benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveals that the proposed tracker not only outperforms existing solutions but also efficiently handle various tracking challenges. On average of outcome, we achieve CLE of 7.98 and F-measure of 0.734.